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accelerate-0.21.0\n"]}],"source":["!pip install transformers==4.28.0\n","!pip install datasets\n","!pip install accelerate -U"]},{"cell_type":"code","source":["!gdown 1a1v06PvGaWmTCWvL33o2C133D90SRzMC # download cw_texts_clean_bert.csv\n","!mkdir output\n","!mkdir /data\n","!mv cw_texts_clean_bert.csv /data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QvRTCsEPvVCY","executionInfo":{"status":"ok","timestamp":1689536806167,"user_tz":420,"elapsed":4924,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"9d057887-84ef-45e8-9509-c7fa1268eaf6"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1a1v06PvGaWmTCWvL33o2C133D90SRzMC\n","To: /content/cw_texts_clean_bert.csv\n","100% 59.5M/59.5M [00:00<00:00, 73.0MB/s]\n"]}]},{"cell_type":"code","source":["# the following lines (177 in total) are directly copied from Haffner et al. (2023), Political Analysis\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import Dataset\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","from sklearn.metrics import mean_absolute_error\n","from transformers import TrainingArguments, Trainer\n","import torch\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import r2_score\n","from numpy.random import seed\n","from tqdm import tqdm\n","seed(1)\n","\n","final = pd.read_csv('/data/cw_texts_clean_bert.csv')\n","\n","print(len(final))\n","final['fatalities_log'] = final['fatalities_log'].astype(float)\n","\n","train = final[final['year'] < 2021]\n","train.reset_index(inplace=True, drop=True)\n","test = final[final['year'] == 2021]\n","test.reset_index(inplace=True, drop=True)\n","\n","train['split_dummy'] = ''\n","train['year'] = train['year'].astype(int)\n","train['month'] = train['month'].astype(int)\n","\n","for i in tqdm(range(len(train))):\n","    if train['year'][i] == 2020 and train['month'][i] > 6:\n","        train['split_dummy'][i] = 1\n","    else:\n","        train['split_dummy'][i] = 0\n","\n","train['split_dummy'].value_counts()\n","\n","train1 = train[train['split_dummy'] == 0]\n","vali = train[train['split_dummy'] == 1]\n","\n","X_train = train1['final_text']\n","y_train = train1['fatalities_log']\n","X_vali = vali['final_text']\n","y_vali = vali['fatalities_log']\n","X_test = test['final_text']\n","y_test = test['fatalities_log']\n","X_train = pd.DataFrame(X_train)\n","X_test = pd.DataFrame(X_test)\n","X_vali = pd.DataFrame(X_vali)\n","y_vali = pd.DataFrame(y_vali)\n","y_train = pd.DataFrame(y_train)\n","y_test = pd.DataFrame(y_test)\n","\n","df_train = pd.concat([X_train, y_train], axis=1)\n","df_val = pd.concat([X_vali, y_vali], axis=1)\n","df_test = pd.concat([X_test, y_test], axis=1)\n","\n","\n","raw_train_ds = Dataset.from_pandas(df_train)\n","raw_val_ds = Dataset.from_pandas(df_val)\n","raw_test_ds = Dataset.from_pandas(df_test)\n","\n","BASE_MODEL = \"snowood1/ConfliBERT-scr-uncased\"\n","LEARNING_RATE = 2e-3\n","MAX_LENGTH = 256\n","BATCH_SIZE = 16\n","EPOCHS = 20\n","\n","tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n","def model_init():\n","    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=1, ignore_mismatched_sizes=True)\n","    for param in model.bert.parameters():\n","        param.requires_grad = False\n","    return model\n","\n","\n","ds = {\"train\": raw_train_ds, \"validation\": raw_val_ds, \"test\": raw_test_ds}\n","\n","\n","def preprocess_function(examples):\n","    label = examples[\"fatalities_log\"]\n","    examples = tokenizer(examples[\"final_text\"], truncation=True, padding=\"max_length\", max_length=256)\n","\n","    # Change this to real number\n","    examples[\"label\"] = float(label)\n","    return examples\n","\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","\n","def compute_metrics_for_regression(eval_pred):\n","    logits, labels = eval_pred\n","    labels = labels.reshape(-1, 1)\n","\n","    mse = mean_squared_error(labels, logits)\n","    mae = mean_absolute_error(labels, logits)\n","    r2 = r2_score(labels, logits)\n","    single_squared_errors = ((logits - labels).flatten() ** 2).tolist()\n","\n","    # Compute accuracy\n","    # Based on the fact that the rounded score = true score only if |single_squared_errors| < 0.5\n","    accuracy = sum([1 for e in single_squared_errors if e < 0.25]) / len(single_squared_errors)\n","\n","    return {\"mse\": mse, \"mae\": mae, \"r2\": r2, \"accuracy\": accuracy}\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/data/output/conflibert\",\n","    learning_rate=LEARNING_RATE,\n","    per_device_train_batch_size=BATCH_SIZE,\n","    per_device_eval_batch_size=BATCH_SIZE,\n","    num_train_epochs=EPOCHS,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=500,\n","    save_total_limit=10,\n","    load_best_model_at_end=True,\n","    weight_decay=0.01,\n","    seed=123\n",")\n","\n","class RegressionTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(**inputs)\n","        logits = outputs[0][:, 0]\n","        loss = torch.nn.functional.mse_loss(logits, labels)\n","        return (loss, outputs) if return_outputs else loss\n","\n","\n","trainer = RegressionTrainer(\n","    model_init=model_init,\n","    args=training_args,\n","    train_dataset=ds[\"train\"],\n","    eval_dataset=ds[\"validation\"],\n","    compute_metrics=compute_metrics_for_regression,\n",")\n","\n","trainer.train()\n","\n","for obj in trainer.state.log_history:\n","    print(obj)\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()\n","\n","raw_pred_test, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred_test[:20])\n","pd.DataFrame(raw_pred_test).to_csv('/data/cw_pred_test_zero.csv')\n","\n","df_results = pd.DataFrame(columns=['Test_MSE','Test_R2'], index=range(0,1))\n","mse = mean_squared_error(ds['test']['fatalities_log'], raw_pred_test)\n","r_squared = r2_score(ds['test']['fatalities_log'], raw_pred_test)\n","print(mse)\n","print(r_squared)\n","df_results['Test_MSE'][0] = mse\n","df_results['Test_R2'][0] = r_squared\n","df_results.to_csv('/data/bert_metrics.csv', index=False)\n","\n","## Whole CW\n","print('Now whole corpus')\n","raw_train1_ds = Dataset.from_pandas(final)\n","ds = {\"test\": raw_train1_ds}\n","\n","## Create dataset\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","print('Now predicting')\n","raw_pred, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred[:20])\n","pd.DataFrame(raw_pred).to_csv('/data/cw_bert_pred_zero.csv')\n","\n","print('DF saved')\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["37cc3d5441124bdaa01478d03c84e83e","f81d417fd1b04b53bd5d2b2149df765a","362c82e7e8664651a2ecd04fa9377b5c","5f57398857db44a79aaa38a100a60d92","e8cde6f1bb8343afbb6c6890d04214e5","3582705ef299465cb4e3b105f541605a","bcbebc8b15ea43f19e715581d5627753","7d492ce626544dcc94157f2bdc2eb662","f197251b1c92434ea0ebbbca05498905","5e1cf527cfaf4ef6af9e2d3bf1264da5","44dda7f4e04b43db99f5d637dd673d07","48518c001bdc44dd97a1a6e8cc974d24","e6860f054ddf4f5abe1e1f0414cbe00c","419b20c55ca9450fb76ff323d1a69a2d","a428d4aab1784ed0884eda9e3d27a738","8125399062e24be9aa99c77e7294760b","5a3b61d79cdb49b595676330942fb134","ebe66b7c1bfe401a947b7b1b5ff10456","a48e7186604f4acea5c735cf6596f9d8","53fe8c103bca4a1e9500020b66a77ea5","2b7ff3b21d624bca94b332fd0d34bfbd","5a554eebe29d4e44ae39407d41bca2e8","7b2905121c16454a8564c2733cde2a62","a01c127aab2e4a13b8c23bc6e4de047a","566fb7c4689c462b988090f8b5fe5843","b63b324a79b7413180f34de53201aa20","96919d5c8b82408eba4b436d99a08df9","7aa7f0e631374d6488fac8bb980859b2","e7056aa5c8b94ed5a9e2e814840e1d8b","fe28841129c14b3c8d1e654dd7d73ca6","daf0032c21f84ec58848888795ad64f0","0de0c0b02c6c41d1af2702bfacfde0e6","18d52fd4b9474c38896483a099c12ae2","8741503d4adb485887088172756a63c7","51692f01e8ce4ddb8998d92d50df6e95","f896ba3dd7644bf88acc679f632a02ba","0e57c7e0307e4021be0a5ec3f4e877ed","f7e5e5050fa64849aa60fba79f825767","1142b53b1550487ca606125d755bbe07","6676ccabe2dc412cb362d10ecae0a7d0","99af72ecce054a9fb547ec2d025016d8","2c411cb4cfc74bdc81d800e1b56e0acd","7a376ad9af6f49298ac3f40e580f2066","fb99e2b6b92d4c869b49ca72b1faa97e","3bb17069c2b040e9873aca28b147a911","bf85c2b07e924ed98a38b4d99e58ea67","9319fe4443e14733a970a2c05d5cca34","dbc27526317d4a79b95c3bcc62d7bb36","f406745407284ca2a2b81c06ad572fdf","258c8c47a395468b87bce70ea3119066","961e9985110447c5831d6c9237a3f3ba","c24f43df53af4a24a1df1ea3c0c8576a","84158d59e058428fba49d2c6df608448","beeac88d9fdb4fa888b260dc73d21322","c51e3be47852469593ca55075d578328","bf7816716dfc4e688a9ecf1d2aa68cf4","28586d1912524431a7548796fd2698f9","8a429692aacd466c967182f2399a34cc","62b8b69b36f64473bc67afd2a0a384bf","1d4a9fa6bff043c59ab1dcf9f6399690","25eb962bc0804404b7c007c94e74af7e","f46a7e3df7b84a63985cc503d71e045c","2c22e3918a794584ae8fff34eab4bd88","8378ef106d58418281e359ae4260f78f","8f06eabf691d46ce8382ec2b2865943d","f6264fe3d1fe47a08175cde650eb227e","5d5e36996b2d46018bd3143cf26127ae","16355c6951b44065b0ed19445beeb2ad","d76fcfe6f8264e4cb443286bfdc6f46e","4730d1dfa8b24baea3d47bfd2899302c","9474ba258b464942bdb7735cf799c7bc","f3a1fa8a74914ed2802db699132332ee","e57e4d061ac5466c843c25ecb04e977c","63ee2467c43b4c3aaa742fd9344f8d52","37a3eb34cbf346fe8aa6813b74b9b46a","d7be6d1dcdaf4023b26cea755567bf21","6eac968ed818405aa9af1885f3b90297","859258aaff2f4eccb55f8a395b6833b2","ad8063d46f8547f69d27ae135990939b","7e6bcac5edcb45acb47ef41d3e5ef7e4","9775b74053284b8c8ddd8df1e19e4c58","d9e2e3c8ae7e425fa157d1a7d743add8","05322550e3334aa29758a1439d0be0d4","13b1917e53c4466c8e440ca3b67a14c1","a96f4908d6a14f3ca17b90419ead11a5","94fc15164eb74887be2e20a3a3003fc9","4f49d7b9c6744015842574c8c5a1a8cc","64b4639b83cb4a7eaf93bee4cbea7e4f","de41b49f01e5444199e524f1e92454dd","01cbf087e2454371bdc1c98b5c0f7a83","eb708baf705b44e9a9c48738054cce33","dd188d88a93544f8abaec76d7d9b0095","025ee93f4cc8471eb963b3dcdc8edf69","0e74858edd1847a1bbc4b374d9ca3ca7","c6ab1f1cfedb4353ac6bf7ffb39e8d7f","8ba989d1ce794871be383cfcf58bd504","0c10f4d288a0453d8fbc213170372162","950978ec574e4131ba5d802b6fc517ba","8b92017f6abc4ac68152aabd5f440e91","e4d66b53875443849e0a427d6f19efec","117ab4d1679f443cbb869507c4014d34","bb4d8a8207a34bc9bb5946c351a2e588","6a7f7ff035df4a79a2f2932a15f9da18","f755391692fc4d189f055546bfde257f","7ee19ce371ee4b17bf8d7b842102aae0","2dc0fe65e65a413db96655ed2dafad33","9987ae86d5534567bbf2cd5a5fc7a857","57338c48caed488983e3f28f4b1cd615","4bad24f65afe4df2a7d917c974e8c48e","a508016343b94b4ba305d1e56a0bb9ab"]},"id":"_UUtlbRAySQ_","executionInfo":{"status":"ok","timestamp":1689539291588,"user_tz":420,"elapsed":2256054,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"6aa2fff9-e71c-462c-b326-b1642dc83c63"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-4-77642d9fd439>:26: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'] = ''\n","<ipython-input-4-77642d9fd439>:27: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['year'] = train['year'].astype(int)\n","<ipython-input-4-77642d9fd439>:28: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['month'] = train['month'].astype(int)\n"]},{"output_type":"stream","name":"stdout","text":["23868\n"]},{"output_type":"stream","name":"stderr","text":["  0%|          | 0/22572 [00:00<?, ?it/s]<ipython-input-4-77642d9fd439>:34: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 0\n","<ipython-input-4-77642d9fd439>:32: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 1\n","100%|██████████| 22572/22572 [00:06<00:00, 3397.58it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"37cc3d5441124bdaa01478d03c84e83e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"48518c001bdc44dd97a1a6e8cc974d24"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/224k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7b2905121c16454a8564c2733cde2a62"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/454k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8741503d4adb485887088172756a63c7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3bb17069c2b040e9873aca28b147a911"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/21924 [00:00<?, ? 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model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='27420' max='27420' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [27420/27420 33:18, Epoch 20/20]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Mse</th>\n","      <th>Mae</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>1.853100</td>\n","      <td>2.123081</td>\n","      <td>2.123081</td>\n","      <td>1.101620</td>\n","      <td>0.457982</td>\n","      <td>0.212963</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>1.671700</td>\n","      <td>1.971106</td>\n","      <td>1.971106</td>\n","      <td>0.965741</td>\n","      <td>0.496781</td>\n","      <td>0.489198</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>1.634300</td>\n","      <td>1.951382</td>\n","      <td>1.951382</td>\n","      <td>0.948717</td>\n","      <td>0.501816</td>\n","      <td>0.470679</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>1.631900</td>\n","      <td>1.898328</td>\n","      <td>1.898328</td>\n","      <td>0.943360</td>\n","      <td>0.515361</td>\n","      <td>0.544753</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>1.641700</td>\n","      <td>1.944146</td>\n","      <td>1.944145</td>\n","      <td>0.954705</td>\n","      <td>0.503664</td>\n","      <td>0.481481</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>1.643400</td>\n","      <td>1.786088</td>\n","      <td>1.786088</td>\n","      <td>0.885585</td>\n","      <td>0.544015</td>\n","      <td>0.510802</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>1.699400</td>\n","      <td>1.862028</td>\n","      <td>1.862028</td>\n","      <td>0.942962</td>\n","      <td>0.524628</td>\n","      <td>0.486111</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>1.554800</td>\n","      <td>1.793568</td>\n","      <td>1.793568</td>\n","      <td>0.831034</td>\n","      <td>0.542106</td>\n","      <td>0.546296</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>1.558200</td>\n","      <td>1.830892</td>\n","      <td>1.830892</td>\n","      <td>0.926037</td>\n","      <td>0.532577</td>\n","      <td>0.461420</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>1.651500</td>\n","      <td>1.833631</td>\n","      <td>1.833631</td>\n","      <td>0.881114</td>\n","      <td>0.531878</td>\n","      <td>0.547840</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>1.635700</td>\n","      <td>1.820882</td>\n","      <td>1.820883</td>\n","      <td>0.864692</td>\n","      <td>0.535132</td>\n","      <td>0.526235</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>1.614600</td>\n","      <td>1.867728</td>\n","      <td>1.867728</td>\n","      <td>0.879318</td>\n","      <td>0.523173</td>\n","      <td>0.515432</td>\n","    </tr>\n","    <tr>\n","      <td>6500</td>\n","      <td>1.602300</td>\n","      <td>1.945784</td>\n","      <td>1.945784</td>\n","      <td>0.990320</td>\n","      <td>0.503245</td>\n","      <td>0.279321</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>1.583300</td>\n","      <td>1.875056</td>\n","      <td>1.875056</td>\n","      <td>0.910656</td>\n","      <td>0.521302</td>\n","      <td>0.472222</td>\n","    </tr>\n","    <tr>\n","      <td>7500</td>\n","      <td>1.548500</td>\n","      <td>1.798769</td>\n","      <td>1.798769</td>\n","      <td>0.865396</td>\n","      <td>0.540778</td>\n","      <td>0.533951</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>1.631200</td>\n","      <td>1.775699</td>\n","      <td>1.775699</td>\n","      <td>0.857177</td>\n","      <td>0.546668</td>\n","      <td>0.532407</td>\n","    </tr>\n","    <tr>\n","      <td>8500</td>\n","      <td>1.602600</td>\n","      <td>1.955714</td>\n","      <td>1.955714</td>\n","      <td>0.958141</td>\n","      <td>0.500710</td>\n","      <td>0.486111</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>1.619400</td>\n","      <td>1.849923</td>\n","      <td>1.849923</td>\n","      <td>0.858351</td>\n","      <td>0.527718</td>\n","      <td>0.561728</td>\n","    </tr>\n","    <tr>\n","      <td>9500</td>\n","      <td>1.591700</td>\n","      <td>1.904479</td>\n","      <td>1.904479</td>\n","      <td>0.935750</td>\n","      <td>0.513790</td>\n","      <td>0.493827</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>1.594800</td>\n","      <td>1.853913</td>\n","      <td>1.853913</td>\n","      <td>0.890204</td>\n","      <td>0.526700</td>\n","      <td>0.513889</td>\n","    </tr>\n","    <tr>\n","      <td>10500</td>\n","      <td>1.598900</td>\n","      <td>1.893321</td>\n","      <td>1.893321</td>\n","      <td>0.919916</td>\n","      <td>0.516639</td>\n","      <td>0.415123</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>1.563400</td>\n","      <td>1.883947</td>\n","      <td>1.883947</td>\n","      <td>0.908818</td>\n","      <td>0.519032</td>\n","      <td>0.424383</td>\n","    </tr>\n","    <tr>\n","      <td>11500</td>\n","      <td>1.559600</td>\n","      <td>1.800808</td>\n","      <td>1.800808</td>\n","      <td>0.872145</td>\n","      <td>0.540257</td>\n","      <td>0.486111</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>1.575600</td>\n","      <td>1.991376</td>\n","      <td>1.991376</td>\n","      <td>0.953668</td>\n","      <td>0.491606</td>\n","      <td>0.469136</td>\n","    </tr>\n","    <tr>\n","      <td>12500</td>\n","      <td>1.594300</td>\n","      <td>1.787206</td>\n","      <td>1.787206</td>\n","      <td>0.860931</td>\n","      <td>0.543730</td>\n","      <td>0.521605</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>1.522200</td>\n","      <td>1.806684</td>\n","      <td>1.806684</td>\n","      <td>0.879759</td>\n","      <td>0.538757</td>\n","      <td>0.549383</td>\n","    </tr>\n","    <tr>\n","      <td>13500</td>\n","      <td>1.608800</td>\n","      <td>1.909232</td>\n","      <td>1.909232</td>\n","      <td>0.911263</td>\n","      <td>0.512577</td>\n","      <td>0.472222</td>\n","    </tr>\n","    <tr>\n","      <td>14000</td>\n","      <td>1.582000</td>\n","      <td>1.792704</td>\n","      <td>1.792704</td>\n","      <td>0.871484</td>\n","      <td>0.542326</td>\n","      <td>0.455247</td>\n","    </tr>\n","    <tr>\n","      <td>14500</td>\n","      <td>1.568400</td>\n","      <td>1.847733</td>\n","      <td>1.847733</td>\n","      <td>0.905989</td>\n","      <td>0.528277</td>\n","      <td>0.530864</td>\n","    </tr>\n","    <tr>\n","      <td>15000</td>\n","      <td>1.544200</td>\n","      <td>1.852603</td>\n","      <td>1.852603</td>\n","      <td>0.916577</td>\n","      <td>0.527034</td>\n","      <td>0.496914</td>\n","    </tr>\n","    <tr>\n","      <td>15500</td>\n","      <td>1.528400</td>\n","      <td>1.847083</td>\n","      <td>1.847082</td>\n","      <td>0.900288</td>\n","      <td>0.528444</td>\n","      <td>0.503086</td>\n","    </tr>\n","    <tr>\n","      <td>16000</td>\n","      <td>1.572500</td>\n","      <td>1.805052</td>\n","      <td>1.805052</td>\n","      <td>0.877348</td>\n","      <td>0.539174</td>\n","      <td>0.509259</td>\n","    </tr>\n","    <tr>\n","      <td>16500</td>\n","      <td>1.605200</td>\n","      <td>1.806047</td>\n","      <td>1.806047</td>\n","      <td>0.868957</td>\n","      <td>0.538920</td>\n","      <td>0.541667</td>\n","    </tr>\n","    <tr>\n","      <td>17000</td>\n","      <td>1.552400</td>\n","      <td>1.917272</td>\n","      <td>1.917272</td>\n","      <td>0.911931</td>\n","      <td>0.510524</td>\n","      <td>0.484568</td>\n","    </tr>\n","    <tr>\n","      <td>17500</td>\n","      <td>1.553500</td>\n","      <td>1.922088</td>\n","      <td>1.922088</td>\n","      <td>0.918085</td>\n","      <td>0.509295</td>\n","      <td>0.424383</td>\n","    </tr>\n","    <tr>\n","      <td>18000</td>\n","      <td>1.538600</td>\n","      <td>1.834678</td>\n","      <td>1.834678</td>\n","      <td>0.879883</td>\n","      <td>0.531610</td>\n","      <td>0.504630</td>\n","    </tr>\n","    <tr>\n","      <td>18500</td>\n","      <td>1.605400</td>\n","      <td>1.924078</td>\n","      <td>1.924078</td>\n","      <td>0.953398</td>\n","      <td>0.508787</td>\n","      <td>0.523148</td>\n","    </tr>\n","    <tr>\n","      <td>19000</td>\n","      <td>1.544300</td>\n","      <td>1.862730</td>\n","      <td>1.862730</td>\n","      <td>0.882049</td>\n","      <td>0.524449</td>\n","      <td>0.504630</td>\n","    </tr>\n","    <tr>\n","      <td>19500</td>\n","      <td>1.571400</td>\n","      <td>1.781885</td>\n","      <td>1.781885</td>\n","      <td>0.847924</td>\n","      <td>0.545088</td>\n","      <td>0.521605</td>\n","    </tr>\n","    <tr>\n","      <td>20000</td>\n","      <td>1.581100</td>\n","      <td>1.815053</td>\n","      <td>1.815053</td>\n","      <td>0.881454</td>\n","      <td>0.536620</td>\n","      <td>0.503086</td>\n","    </tr>\n","    <tr>\n","      <td>20500</td>\n","      <td>1.548400</td>\n","      <td>1.833412</td>\n","      <td>1.833412</td>\n","      <td>0.877664</td>\n","      <td>0.531934</td>\n","      <td>0.506173</td>\n","    </tr>\n","    <tr>\n","      <td>21000</td>\n","      <td>1.561900</td>\n","      <td>1.831032</td>\n","      <td>1.831032</td>\n","      <td>0.901116</td>\n","      <td>0.532541</td>\n","      <td>0.500000</td>\n","    </tr>\n","    <tr>\n","      <td>21500</td>\n","      <td>1.571100</td>\n","      <td>1.842297</td>\n","      <td>1.842297</td>\n","      <td>0.903024</td>\n","      <td>0.529665</td>\n","      <td>0.510802</td>\n","    </tr>\n","    <tr>\n","      <td>22000</td>\n","      <td>1.478200</td>\n","      <td>1.921203</td>\n","      <td>1.921203</td>\n","      <td>0.909570</td>\n","      <td>0.509521</td>\n","      <td>0.493827</td>\n","    </tr>\n","    <tr>\n","      <td>22500</td>\n","      <td>1.502700</td>\n","      <td>1.818316</td>\n","      <td>1.818316</td>\n","      <td>0.873975</td>\n","      <td>0.535787</td>\n","      <td>0.538580</td>\n","    </tr>\n","    <tr>\n","      <td>23000</td>\n","      <td>1.529600</td>\n","      <td>1.832505</td>\n","      <td>1.832505</td>\n","      <td>0.896922</td>\n","      <td>0.532165</td>\n","      <td>0.526235</td>\n","    </tr>\n","    <tr>\n","      <td>23500</td>\n","      <td>1.561700</td>\n","      <td>1.842917</td>\n","      <td>1.842917</td>\n","      <td>0.890042</td>\n","      <td>0.529507</td>\n","      <td>0.515432</td>\n","    </tr>\n","    <tr>\n","      <td>24000</td>\n","      <td>1.516000</td>\n","      <td>1.863640</td>\n","      <td>1.863640</td>\n","      <td>0.903489</td>\n","      <td>0.524217</td>\n","      <td>0.509259</td>\n","    </tr>\n","    <tr>\n","      <td>24500</td>\n","      <td>1.549900</td>\n","      <td>1.833424</td>\n","      <td>1.833424</td>\n","      <td>0.890301</td>\n","      <td>0.531930</td>\n","      <td>0.523148</td>\n","    </tr>\n","    <tr>\n","      <td>25000</td>\n","      <td>1.579800</td>\n","      <td>1.835725</td>\n","      <td>1.835725</td>\n","      <td>0.879666</td>\n","      <td>0.531343</td>\n","      <td>0.513889</td>\n","    </tr>\n","    <tr>\n","      <td>25500</td>\n","      <td>1.579900</td>\n","      <td>1.832587</td>\n","      <td>1.832587</td>\n","      <td>0.864495</td>\n","      <td>0.532144</td>\n","      <td>0.509259</td>\n","    </tr>\n","    <tr>\n","      <td>26000</td>\n","      <td>1.482600</td>\n","      <td>1.831896</td>\n","      <td>1.831896</td>\n","      <td>0.874075</td>\n","      <td>0.532321</td>\n","      <td>0.516975</td>\n","    </tr>\n","    <tr>\n","      <td>26500</td>\n","      <td>1.555300</td>\n","      <td>1.836328</td>\n","      <td>1.836328</td>\n","      <td>0.871353</td>\n","      <td>0.531189</td>\n","      <td>0.504630</td>\n","    </tr>\n","    <tr>\n","      <td>27000</td>\n","      <td>1.489000</td>\n","      <td>1.823130</td>\n","      <td>1.823129</td>\n","      <td>0.871422</td>\n","      <td>0.534559</td>\n","      <td>0.518519</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'loss': 1.8531, 'learning_rate': 0.001963530269876003, 'epoch': 0.36, 'step': 500}\n","{'eval_loss': 2.1230814456939697, 'eval_mse': 2.1230814456939697, 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object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[[ 6.5594854 ]\n"," [ 5.5609636 ]\n"," [ 5.7771072 ]\n"," [ 6.049673  ]\n"," [ 6.239936  ]\n"," [ 6.597352  ]\n"," [ 7.0742292 ]\n"," [ 3.6066666 ]\n"," [ 5.247986  ]\n"," [ 6.4203634 ]\n"," [ 5.104023  ]\n"," [ 4.4602947 ]\n"," [-0.21876687]\n"," [ 2.1585412 ]\n"," [-0.21876687]\n"," [-0.21876687]\n"," [-0.21876687]\n"," [-0.21876687]\n"," [-0.21876687]\n"," [-0.21876687]]\n","1.7503021153174287\n","0.6003749194371616\n","Now whole corpus\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/23868 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e4d66b53875443849e0a427d6f19efec"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Now predicting\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[[4.896306 ]\n"," [4.5448217]\n"," [4.3618374]\n"," [4.0598936]\n"," [3.9939537]\n"," [3.7218077]\n"," [4.8781605]\n"," [4.336542 ]\n"," [5.9259787]\n"," [4.780677 ]\n"," [4.998246 ]\n"," [3.3160572]\n"," [5.7578297]\n"," [3.393884 ]\n"," [3.555465 ]\n"," [3.3076053]\n"," [3.1232014]\n"," [4.180085 ]\n"," [4.14186  ]\n"," [3.0477366]]\n","DF saved\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1492' max='1492' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1492/1492 01:31]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 1.4192008972167969,\n"," 'eval_mse': 1.4192008972167969,\n"," 'eval_mae': 0.7923488616943359,\n"," 'eval_r2': 0.5962680816966575,\n"," 'eval_accuracy': 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